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Core Banking–Focused PT-SLM Implementation Plan & Workflow Chart

Implementation Plan

  1. Project Assessment and Scoping

    • Identify core banking areas with the highest potential for AI enhancement (e.g., transaction processing, loan servicing, payment settlements).
    • Define clear goals: improve accuracy, reduce processing time, strengthen compliance, or enhance customer notifications.
    • Assemble a cross-functional team including IT, security, compliance, and business operations.

  2. Infrastructure Readiness

    • Ensure that on-premise hardware or a secure private cloud environment is available to host PT-SLM models.
    • Validate secure connectors to core banking systems (databases, ERP modules, payment gateways).
    • Conduct a network security assessment: set up internal firewalls, role-based access, encryption standards, and segment the PT-SLM environment.

  3. Model Selection and Customization

    • Choose or build a PT-SLM that can handle core banking–specific language tasks.
    • Fine-tune the model using bank-specific datasets (e.g., anonymized transaction logs, loan records, payment patterns).
    • Implement a prompt validation and data anonymization layer to prevent any accidental leakage of sensitive data.

  4. Integration with Core Banking Systems

    • Connect PT-SLM securely to critical modules (account management, payments, loans) using encrypted APIs or middleware.
    • Define workflows where AI outputs directly enhance or automate tasks (e.g., flagging suspicious transactions, automating loan approvals, validating payments).
    • Ensure fallback and human oversight mechanisms are in place for high-risk decisions.

  5. Testing and Validation

    • Conduct sandbox testing with synthetic data to validate performance, accuracy, and security.
    • Run pilot deployments in limited-use environments (e.g., internal-only use cases) before full-scale rollout.
    • Engage compliance and audit teams to review system outputs and ensure regulatory alignment.

  6. Deployment and Monitoring

    • Deploy PT-SLM into the production environment with full monitoring.
    • Set up dashboards and alerts for model performance, decision outputs, and system health.
    • Establish regular review cycles to retrain models, update datasets, and patch security issues.

Data Ingestion Layer

Core Banking PT-SLM Workflow Chart

1️⃣ Data Ingestion Layer → Securely collects data from core banking systems (transaction databases, loan records, payment gateways).

2️⃣ Prompt Validation & Anonymization Layer → Filters and sanitizes input data to remove sensitive details and ensure regulatory compliance.

3️⃣ PT-SLM Processing Layer → Executes tailored language tasks.

  • Transaction flagging
  • Loan evaluation summaries
  • Payment validation

4️⃣ Output Integration Layer → Feeds results back into core banking modules via secure APIs, updating dashboards, triggering automated workflows, or generating compliance reports.

5️⃣ Monitoring & Feedback Layer → Tracks system performance, logs decisions, monitors accuracy, and routes flagged cases for human review when necessary.

6️⃣ Continuous Improvement Loop → Periodic retraining and updates based on new data, audit findings, and evolving regulatory requirements.

Banking implementation

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